Introduction and Motivation
Linear Algebra
Analytic Geometry
Matrix Decompositions
Vector Calculus
Probability and Distribution
Continuous Optimization
Part II: Central Machine Learning Problems
When Models Meet Data
Linear Regression
Dimensionality Reduction with Principal Component Analysis
Density Estimation with Gaussian Mixture Models
Classification with Support Vector Machines
· · · · · · (收起)
具體描述
https://mml-book.github.io/
::This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics::
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
用戶評價
##寫的不錯,難度適中
評分##相較而言我更喜歡前半部分有關於數學基礎的部分，深入淺齣。
評分##雖然很基礎，但是對於有些東西經常會給齣多種角度的解釋，總有一種能讓人容易理解和接受，還不錯的書，但是如果花太長時間看就比較不值得
評分##劍橋齣版的書文風總是規整一些，讀起來排版很美。前麵小錯誤不少，網站上給瞭校正。
評分##過淺, 隻適閤速覽
評分##不管是拿來入門還是重溫都很適閤
評分##市麵上最好的機器學習入門教材（我菜我先說）
評分##part1介紹ml裏頻繁用到的數學，part2再介紹幾個具有代錶性的ml算法，知識編排非常閤理。 想打十分，感覺很適閤拿來入門，但即使是重溫（比如我）也會有收獲，太喜歡作者的寫作風格瞭。
評分##特彆適閤像我這種已經n年沒學過數學的人，也很適閤做reference有什麼不懂的時候即興翻翻
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